CN110517077A - Commodity similarity analysis method, apparatus and storage medium based on attributive distance - Google Patents
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Abstract
The embodiment of the invention discloses a kind of commodity similarity analysis method, apparatus and storage medium based on attributive distance, method include: to choose any two commodity data as commodity data pair to be analyzed；To commodity data to be analyzed to attributes extraction processing is carried out, to obtain commodity inherent nature；Based on the commodity inherent nature, to commodity data to be analyzed to the processing of multilayer distance algorithm is carried out, to obtain multiple distance values；Multiple distance values are inputted into preset prediction model, to obtain the similarity value of commodity data pair to be analyzed.Through the above technical solutions, the similar commodity in chaotic data can be fast and accurately identified, Artificial Cognition is carried out according to priori knowledge without practitioner, the accuracy of commodity similarity identification is improved, also improves working efficiency.
Description
Technical field
The present invention relates to data statistic analysis technical fields, and in particular to a kind of commodity similarity based on attributive distance point
Analyse method, apparatus and storage medium.
Background technique
Modern society's type of merchandize is various, and for wholesale commodity on the market because many factors influence, it is low that price has height to have,
Merchandise sales practitioner is highly desirable to obtain a kind of ability for distinguishing identical commodity, to reach increase working achievement.It is existing
The problem of be: be difficult to find whether commodity are the same commodity, about commodity similarity data analysis result inaccuracy, working
Staff recognizes commodity and needs many priori knowledges, analysis method also most existing defects.
Specifically, existing identification scheme lacks, and is mostly based on product name similarity calculation, this method accuracy is not high；
Practitioner analyzes the method for commodity also due to information excavating channel and focus are often with subjectivity, data analyst meeting
Go to collect data towards expected subconsciousness judgement, so analysis result is often not accurate enough, finally result in can not be the discovery that it is same
The floating of a commodity price.
Therefore, it is competed between similar commodity and brings immense pressure to practitioner, accurately quickly finding has competition
A pair of of commodity of relationship necessarily can for industry product the market expansion and reduce cost and bring important reference.
Summary of the invention
The embodiment of the present invention be designed to provide a kind of commodity similarity analysis method, apparatus based on attributive distance and
Storage medium improves working efficiency to improve the accuracy of commodity similarity identification.
To achieve the above object, in a first aspect, the embodiment of the invention provides a kind of commodity based on attributive distance are similar
Spend analysis method, comprising:
Any two commodity data is chosen as commodity data pair to be analyzed；
To the commodity data to be analyzed to attributes extraction processing is carried out, to obtain commodity inherent nature；
Based on the commodity inherent nature, to the commodity data to be analyzed to the processing of multilayer distance algorithm is carried out, to obtain
To multiple distance values；
Multiple distance values are inputted into preset prediction model, to obtain the similarity of the commodity data pair to be analyzed
Value.
As a kind of specific embodiment of the application, to the commodity data to be analyzed to carrying out attributes extraction processing,
To obtain commodity inherent nature, specifically include:
Word segmentation processing is carried out to the product name of the commodity data pair to be analyzed, to extract the commodity inherent nature.
As a kind of specific embodiment of the application, it is based on the commodity inherent nature, to the commodity number to be analyzed
It is specifically included according to progress multilayer distance algorithm processing with obtaining multiple distance values:
Vectorization processing is carried out to the commodity inherent nature；
After editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
The commodity inherent nature carries out multilayer distance and calculates, to obtain multiple distance values.
Wherein, the commodity inherent nature includes product name, brand, single-item specification, sells specification, single-item unit, packet
Number, model or taste in filling carry out vectorization processing to the commodity inherent nature and specifically include:
Using tf-idf or n-gram algorithm to product name, brand, sell specification, model and taste carry out at vectorization
Reason；
Vectorization processing is carried out to single-item unit and single-item specification using 01 matching way.
Further, as a kind of preferred embodiment of the application, the commodity similarity analysis method further includes instruction
Practice the prediction model, specifically include:
Sample data is obtained, the sample data includes the commodity pair that two similarity values are more than threshold value；
Attributes extraction processing is carried out to the sample data, to obtain sample inherent nature；
Vectorization processing is carried out to the sample inherent nature using tf-idf or n-gram algorithm or 01 matching way；
After editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
The sample inherent nature carries out multilayer distance and calculates, to obtain multiple sample distance values；
According to multiple sample distance values, logistic regression training is carried out to the sample data using machine learning method, with
Obtain the prediction model.
As a kind of specific embodiment of the application, according to multiple sample distance values, using machine learning method to institute
It states sample data and carries out logistic regression training, to obtain the prediction model, specifically include:
Distance matrix is established according to multiple sample distance values；
Machine learning method is used to carry out logistic regression training to solve multiple Attribute Weight weight values；
The prediction model is determined according to multiple Attribute Weight weight values.
Second aspect, the embodiment of the present application also provides a kind of commodity similarity analysis device based on attributive distance, packet
It includes:
Module is chosen, for choosing any two commodity data as commodity data pair to be analyzed；
Extraction module is used for the commodity data to be analyzed to attributes extraction processing is carried out, to obtain the potential category of commodity
Property；
Computing module, for being based on the commodity inherent nature, to the commodity data to be analyzed to progress multilayer distance
Algorithm process, to obtain multiple distance values；
Prediction module, for multiple distance values to be inputted preset prediction model, to obtain the commodity to be analyzed
The similarity value of data pair.
Further, which further includes training module, for training the prediction model, specifically
Include:
Sample data is obtained, the sample data includes the commodity pair that two similarity values are more than threshold value；
Attributes extraction processing is carried out to the sample data, to obtain sample inherent nature；
Vectorization processing is carried out to the sample inherent nature using tf-idf or n-gram algorithm or 01 matching way；
After editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
The sample inherent nature carries out multilayer distance and calculates, to obtain multiple sample distance values；
According to multiple sample distance values, logistic regression training is carried out to the sample data using machine learning method, with
Obtain the prediction model.
The third aspect, the embodiment of the invention also provides a kind of commodity similarity analysis device based on attributive distance, packet
Processor, input equipment, output equipment and memory are included, the processor, input equipment, output equipment and memory mutually interconnect
It connects, wherein the memory is for storing computer program, and the computer program includes program instruction, the processor quilt
It is configured to call described program instruction, the method for executing above-mentioned first aspect.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored with computer
Program, the computer program include program instruction, and described program instruction when being executed by a processor executes the processor
The method of above-mentioned first aspect.
Implement the embodiment of the present invention, first extracts the commodity inherent nature of commodity data pair to be analyzed, it is potential based on the commodity
Attribute carries out the processing of multilayer distance algorithm to obtain multiple distance values, and multiple distance values are finally inputted preset prediction model,
To obtain the similarity value of the commodity data pair to be analyzed；Through the above technical solutions, confusion can be fast and accurately identified
Similar commodity in data carry out Artificial Cognition according to priori knowledge without practitioner, improve commodity similarity identification
Accuracy also improves working efficiency.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described.
Fig. 1 is the exemplary flow for the commodity similarity analysis method based on attributive distance that first embodiment of the invention provides
Figure；
Fig. 2 is the sub-process figure of step S101 in Fig. 1；
Fig. 3 is the structural representation for the commodity similarity analysis device based on attributive distance that one embodiment of the invention provides
Figure；
Fig. 4 be another embodiment of the present invention provides the commodity similarity analysis method based on attributive distance structural representation
Figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hair
Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
It should be appreciated that ought use in this specification and in the appended claims, term " includes " and "comprising" instruction
Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded
Body, step, operation, the presence or addition of element, component and/or its set.
Referring to FIG. 1, being the commodity similarity analysis method based on attributive distance that first embodiment of the invention provides.Such as
Shown in figure, this method be may comprise steps of:
S101, training prediction model.
Specifically, as shown in Fig. 2, step S101 includes:
S1011 obtains sample data.
In the present embodiment, in the shiploads of merchandise data of known similarity value, two similarity values are chosen more than threshold value
The commodity of (such as more than 98%, i.e. similarity value is higher) are to as sample data.Understandably, sample data is actually one
A training sample set, interior includes the higher commodity pair of multiple similarity values.
S1012 carries out attributes extraction processing to sample data, to obtain sample inherent nature.
Specifically, word segmentation processing is carried out to the product name in sample data, to extract sample inherent nature.
S1013 carries out vectorization processing to sample inherent nature using tf-idf or n-gram algorithm or 01 matching way.
Sample inherent nature includes but are not limited to product name, brand, single-item specification, sells specification, single-item unit, packet
Number, model or taste etc. in filling.In the present embodiment, to product name, brand, sells specification, model and taste this several and make
Vectorization processing is carried out with tf-idf or n-gram algorithm, and single-item unit and single-item specification then do 01 vectorization of exact matching,
Wherein, the data of missing are not involved in calculating.For example, if the single-item unit of two commodity is just the same, directly matching is
1, different then matching is 0.
S1014, using editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm to vectorization at
Sample inherent nature after reason carries out multilayer distance and calculates, to obtain multiple sample distance values.
Now the formula about Attribute Recognition is described as follows:
From identification item property algorithm:
Setting X sequence, random sequence Y probability P (Y | X)
X={ X1,X2…Xn, Y={ Y1,Y2…Yn}
Wherein, tk(Yi-1,Yi, X, i) and indicate transfer function tk, indicate that sequence Y is corresponding in position i-1 and i under sequence X
Value transition probability, and sl(Yi, X, i) and indicate function of state sl, indicate that sequence Y is in the corresponding value probability of position i under sequence X.Separately
Outer λk,μlThe weight of respectively two functions.tk(Yi-1,Yi, X, i), k=1,2,3 ... K, K are defined in the part spy of the node
The total number of function is levied, i is present node in the position of sequence；sl(Yi, X, i), l=1,2,3 ... L, L are defined in the node
Node diagnostic function total number, i is present node in the position of sequence.
Enable sl=tk, to feature, simultaneously normalizing is obtained for i summation at various locations:
F (Y, X) is function name.
Return the extensive factor:
In the present embodiment, for doing vectorization using tfidf and ngram when attribute is natural language.Two-value distance
Formula:
Set item property k=1,2,3,4K
Self-defining attribute condition distance:
Editing distance algorithmic notation is as follows:
Wherein S1, S2 are sentence, and i, j are the position in sentence
COS distance algorithmic notation is as follows:
Wherein a, b term vector
It is as follows to optimize crf condition random field distance algorithm:
Characteristic evaluating function: Φi,j(xi,j, λ) and=exp { xi,jλT}
Wherein X is that a pair of of short sentence sentence forms two-dimensional matrix term vector, and i, j are matrix position, and λ is weight parameter, and T is matrix
Transposition.
For example, editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm can be used to do for product name
Distance calculates, so that the analysis for commodity similarity provides fundamental parameter.
S1015 establishes distance matrix according to multiple sample distance values.
S1016 uses machine learning method to carry out logistic regression training to solve multiple Attribute Weight weight values.
S1017 determines prediction model according to multiple Attribute Weight weight values.
S102 chooses any two commodity data as commodity data pair to be analyzed.
S103, commodity data to be analyzed is to attributes extraction processing is carried out, to obtain commodity inherent nature.
Specifically, word segmentation processing is carried out to the product name of analysis commodity data centering, to extract commodity inherent nature
S104, is based on commodity inherent nature, and commodity data to be analyzed is multiple to obtain to progress multilayer distance algorithm processing
Distance value.
In the step, vectorization processing first is carried out to commodity inherent nature, then carry out multilayer algorithm process.
Wherein, commodity inherent nature includes but are not limited to product name, brand, single-item specification, sells specification, single-item list
Number, model or taste etc. in position, packaging.In the present embodiment, to product name, brand, selling specification, model and taste, this is several
Item carries out vectorization processing using tf-idf or n-gram algorithm, and single-item unit and single-item specification then do 01 vector of exact matching
Change, wherein the data of missing are not involved in calculating.For example, it if the single-item unit of two commodity is just the same, directly matches
It is 1, different then matching is 0.
Further, using editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm to vectorization
Treated, and commodity inherent nature carries out the processing of multilayer distance algorithm, to obtain multiple distance values.The used tool of the part
Body algorithm please refers to aforementioned sample data part, and details are not described herein.
Multiple distance values are inputted preset prediction model, to obtain the similar of the commodity data pair to be analyzed by S105
Angle value.
Implement the commodity similarity analysis method based on attributive distance provided by the embodiment of the present invention, using machine learning
Algorithm trains prediction model, first extracts the commodity inherent nature of commodity data pair to be analyzed, is carried out based on the commodity inherent nature
Multiple distance values are finally inputted preset prediction model, to obtain to obtain multiple distance values by the processing of multilayer distance algorithm
State the similarity value of commodity data pair to be analyzed；Through the above technical solutions, can be fast and accurately identified in chaotic data
Similar commodity carry out Artificial Cognition according to priori knowledge without practitioner, improve the accuracy of commodity similarity identification,
It improves work efficiency.
Based on identical inventive concept, the embodiment of the present invention also provides a kind of commodity similarity analysis based on attributive distance
Device.As shown in figure 3, the commodity similarity analysis device includes:
Training module 10, for training prediction model；
Module 11 is chosen, for choosing any two commodity data as commodity data pair to be analyzed；
Extraction module 12 is used for the commodity data to be analyzed to progress attributes extraction processing, potential to obtain commodity
Attribute；
Computing module 13, for be based on the commodity inherent nature, to the commodity data to be analyzed to carry out multilayer away from
From algorithm process, to obtain multiple distance values；
Prediction module 14, for multiple distance values to be inputted preset prediction model, to obtain the quotient to be analyzed
The similarity value of product data pair.
Specifically, in the present embodiment, training module 10 is specifically used for:
Sample data is obtained, the sample data includes the commodity pair that two similarity values are more than threshold value；
Attributes extraction processing is carried out to the sample data, to obtain sample inherent nature；
Vectorization processing is carried out to the sample inherent nature using tf-idf or n-gram algorithm or 01 matching way；
After editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
The sample inherent nature carries out multilayer distance and calculates, to obtain multiple sample distance values；
According to multiple sample distance values, logistic regression training is carried out to the sample data using machine learning method, with
Obtain the prediction model.
Further, above-mentioned commodity inherent nature includes but are not limited to product name, brand, single-item specification, sells rule
Lattice, single-item unit, number, model or taste etc. in packaging, computing module 13 is specifically used for:
Using tf-idf or n-gram algorithm to product name, brand, sell specification, model and taste carry out at vectorization
Reason；
Vectorization processing is carried out to single-item unit and single-item specification using 01 matching way；
After editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
The commodity inherent nature carries out multilayer distance and calculates, to obtain multiple distance values.
It should be noted that the specific workflow of the present embodiment please refers to preceding method embodiment part, herein no longer
It repeats.
Further, another embodiment of the present invention additionally provides a kind of commodity similarity analysis dress based on attributive distance
It sets.As shown in figure 4, should commodity similarity analysis device based on attributive distance, may include: one or more processors 101,
One or more input equipments 102, one or more output equipments 103 and memory 104, above-mentioned processor 101, input equipment
102, output equipment 103 and memory 104 are connected with each other by bus 105.Memory 104 is for storing computer program, institute
Stating computer program includes program instruction, and the processor 101 is configured for calling the described program instruction execution above method
The method of embodiment part.
It should be appreciated that in embodiments of the present invention, alleged processor 101 can be central processing unit (Central
Processing Unit, CPU), which can also be other general processors, digital signal processor (Digital
Signal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit,
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic
Device, discrete gate or transistor logic, discrete hardware components etc..General processor can be microprocessor or this at
Reason device is also possible to any conventional processor etc..
Input equipment 102 may include keyboard etc., and output equipment 103 may include display (LCD etc.), loudspeaker etc..
The memory 104 may include read-only memory and random access memory, and to processor 101 provide instruction and
Data.The a part of of memory 104 can also include nonvolatile RAM.For example, memory 104 can also be deposited
Store up the information of device type.
In the specific implementation, processor 101 described in the embodiment of the present invention, input equipment 102, output equipment 103 can
Execute realization described in the embodiment of the commodity similarity analysis method provided in an embodiment of the present invention based on attributive distance
Mode, details are not described herein.
Implement the commodity similarity analysis device based on attributive distance provided by the embodiment of the present invention, using machine learning
Algorithm trains prediction model, first extracts the commodity inherent nature of commodity data pair to be analyzed, is carried out based on the commodity inherent nature
Multiple distance values are finally inputted preset prediction model, to obtain to obtain multiple distance values by the processing of multilayer distance algorithm
State the similarity value of commodity data pair to be analyzed；Through the above technical solutions, can be fast and accurately identified in chaotic data
Similar commodity carry out Artificial Cognition according to priori knowledge without practitioner, improve the accuracy of commodity similarity identification,
It improves work efficiency.
Correspondingly, the embodiment of the invention provides a kind of computer readable storage medium, the computer-readable storage mediums
Matter is stored with computer program, and the computer program includes program instruction, and described program instructs realization when being executed by processor:
The above-mentioned commodity similarity analysis method based on attributive distance.
The computer readable storage medium can be the internal storage unit of system described in aforementioned any embodiment, example
Such as the hard disk or memory of system.The computer readable storage medium is also possible to the External memory equipment of the system, such as
The plug-in type hard disk being equipped in the system, intelligent memory card (Smart Media Card, SMC), secure digital (Secure
Digital, SD) card, flash card (Flash Card) etc..Further, the computer readable storage medium can also be wrapped both
The internal storage unit for including the system also includes External memory equipment.The computer readable storage medium is described for storing
Other programs and data needed for computer program and the system.The computer readable storage medium can be also used for temporarily
When store the data that has exported or will export.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of the unit, only
Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.In addition, shown or discussed phase
Mutually between coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or communication of device or unit
Connection is also possible to electricity, mechanical or other form connections.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.Some or all of unit therein can be selected to realize the embodiment of the present invention according to the actual needs
Purpose.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, is also possible to two or more units and is integrated in one unit.It is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the present invention
Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (10)
1. a kind of commodity similarity analysis method based on attributive distance characterized by comprising
Any two commodity data is chosen as commodity data pair to be analyzed；
To the commodity data to be analyzed to attributes extraction processing is carried out, to obtain commodity inherent nature；
It is more to obtain to the commodity data to be analyzed to progress multilayer distance algorithm processing based on the commodity inherent nature
A distance value；
Multiple distance values are inputted into preset prediction model, to obtain the similarity value of the commodity data pair to be analyzed.
2. commodity similarity analysis method as described in claim 1, which is characterized in that the commodity data to be analyzed into
The processing of row attributes extraction, to obtain commodity inherent nature, specifically includes:
Word segmentation processing is carried out to the product name of the commodity data pair to be analyzed, to extract the commodity inherent nature.
3. commodity similarity analysis method as described in claim 1, which is characterized in that it is based on the commodity inherent nature, it is right
The commodity data to be analyzed specifically includes progress multilayer distance algorithm processing to obtain multiple distance values:
Vectorization processing is carried out to the commodity inherent nature；
Described in after editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
Commodity inherent nature carries out multilayer distance and calculates, to obtain multiple distance values.
4. commodity similarity analysis method as claimed in claim 3, which is characterized in that the commodity inherent nature includes commodity
Title, single-item specification, sells specification, single-item unit, number, model or taste in packaging at brand, to the commodity inherent nature
Vectorization processing is carried out to specifically include:
Using tf-idf or n-gram algorithm to product name, brand, sell specification, model and taste carry out vectorization processing；
Vectorization processing is carried out to single-item unit and single-item specification using 01 matching way.
5. commodity similarity analysis method according to any one of claims 1-4, which is characterized in that the commodity similarity point
Analysis method further includes the trained prediction model, is specifically included:
Sample data is obtained, the sample data includes the commodity pair that two similarity values are more than threshold value；
Attributes extraction processing is carried out to the sample data, to obtain sample inherent nature；
Vectorization processing is carried out to the sample inherent nature using tf-idf or n-gram algorithm or 01 matching way；
Described in after editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
Sample inherent nature carries out multilayer distance and calculates, to obtain multiple sample distance values；
According to multiple sample distance values, logistic regression training is carried out to the sample data using machine learning method, to obtain
The prediction model.
6. commodity similarity analysis method as claimed in claim 5, which is characterized in that according to multiple sample distance values, use
Machine learning method carries out logistic regression training to the sample data, to obtain the prediction model, specifically includes:
Distance matrix is established according to multiple sample distance values；
Machine learning method is used to carry out logistic regression training to solve multiple Attribute Weight weight values；
The prediction model is determined according to multiple Attribute Weight weight values.
7. a kind of commodity similarity analysis device based on attributive distance characterized by comprising
Module is chosen, for choosing any two commodity data as commodity data pair to be analyzed；
Extraction module is used for the commodity data to be analyzed to attributes extraction processing is carried out, to obtain commodity inherent nature；
Computing module, for being based on the commodity inherent nature, to the commodity data to be analyzed to progress multilayer distance algorithm
Processing, to obtain multiple distance values；
Prediction module, for multiple distance values to be inputted preset prediction model, to obtain the commodity data to be analyzed
Pair similarity value.
8. commodity similarity analysis device as claimed in claim 7, which is characterized in that further include training module, for training
The prediction model, specifically includes:
Sample data is obtained, the sample data includes the commodity pair that two similarity values are more than threshold value；
Attributes extraction processing is carried out to the sample data, to obtain sample inherent nature；
Vectorization processing is carried out to the sample inherent nature using tf-idf or n-gram algorithm or 01 matching way；
Described in after editing distance algorithm, COS distance algorithm or optimization crf condition random field algorithm opposite direction quantification treatment
Sample inherent nature carries out multilayer distance and calculates, to obtain multiple sample distance values；
According to multiple sample distance values, logistic regression training is carried out to the sample data using machine learning method, to obtain
The prediction model.
9. a kind of commodity similarity analysis device based on attributive distance, which is characterized in that including processor, input equipment, defeated
Equipment and memory out, the processor, input equipment, output equipment and memory are connected with each other, wherein the memory is used
In storage computer program, the computer program includes program instruction, and the processor is configured for calling described program
Instruction executes method as claimed in claim 6.
10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer journey
Sequence, the computer program include program instruction, and described program instruction executes the processor such as
Method of claim 6.
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